Qué son los cálculos biliares?

The goal of this work, however, is not to focus on individual genes, but rather identify new mechanistic pathways regulating adult neurogenesis. A closer look at figure 6.4 shows some structure to the 1st-level proliferation subnetwork. The known MANGO genes (grey edges in figure6.4), form a star centred on the proliferation phenotype and these fall into two categories; those supported by other layers and those (to the right side of the graph) which are not. These latter genes, although they are known to be involved in modulating precursor proliferation, may not be regulated at the transcript level—and thus did not exhibit similar patterns of expression to genes identified in the other layers. It is interesting to note that the MANGO genes that were supported by other gene-gene edges were also more densely linked by STRING edges. The DC layer (in green), as mentioned above, yielded a very tight cluster of cell cycle genes. The HC layer interactions (in blue), on the other hand, were more broadly distributed, reflecting their origin in a non-perturbed genetic system. The edges derived from the HE layer, the only layer in this study where a systematic environmental perturbation was performed, were also spread throughout the subnetwork. These edges provide a good starting point for the reconstruction of a specifically activity-dependent pathway. Ultimately, however, characterisation of such pathways will require further experimental work to determine the causal directionality of interactions and establish the functional interactions between candidate molecules.

78 Integrated Gene Networks

Figure 6.5: An example candidate data sheet. An overview of relevant resources is provided in a single page to allow rapid in silico screening of candidate genes. From top to bottom, left to right: an excerpt of the most appropriate Allen Brain Atlas section showing the hippocampus with a link to the source page; a network image from the STRING database with a link to the source page; expression in the HE dataset; expression in the DC dataset; QTL map from the HC dataset; QTL map from an unpublished cell culture dataset; a list of GeneRIF headings from mouse, rat and human.

Integrated Gene Networks 79

Discussion

This chapter has brought together the experimental work from all the previous chapters and attempted to integrate these data into a multi-layer gene interaction network. The individual experiments were all different in many respects, measuring different aspects of the phenotype, and yet all do explore the same phenotype—adult hippocampal neurogenesis. Because differ- ent systems have been measured and different methods used, the resulting data sets exhibited distinctive structure. This was evidenced in the number of nodes remaining after thresholding (figure 6.3A) and the range of degree distributions observed (figure6.2). Nevertheless, a sub- stantial overlap exists as shown by the network formed by nodes sharing interactions in more than one of the source experiments. It is interesting to note that most multiedge interactions existed as part of a single subnetwork (figure6.3B). This suggests that, in the cases where interactions do overlap between the different experiments, these are part of a single ‘adult neurogenesis’ component. At first glance this may seem unexpected but, as noted above, the experiments were expressly designed to address the same phenotype, and so it is encouraging that a common transcriptional system can be associated with this phenotype.

Because the experiments providing the network data have been based upon the association of genes with a common phenotype, this phenotype can also be introduced into the network. This approach of mixing physiological and gene expression phenotypes in a network-based analysis is an innovation (see Overall et al., 2009) that is a logical extension of the ‘genetical genomics’ idea of Jansen and Nap (2001). Mixed gene-phenotype networks have been a feature of the GeneNetwork web service (http://genenetwork.org) for some time and are also at the core of the Ontological Discovery Environment (http://geneweaver.org). To date, however, very few published studies have used this technique (Overall et al.,2009;Roth et al.,2013) and none to my knowledge has incorporated phenotype-to-gene associations in multiple layers.

The work in this chapter has presented a substantial number (149) of genes for which a role in the regulation of adult neural precursor proliferation seems likely—but for which no published report yet exists directly implicating them in the genetic control of this phenotype. The next challenge is to characterise the effect of these genes on adult hippocampal neurogenesis and to establish how they work together to regulate precursor cell proliferation. A necessary part of this process will be verification of a causative role in neurogenesis as many candidates will likely be false positives—appearing in the high-throughput screens due to experimental noise or as a result of a downstream, non-causal association with effector genes. It is therefore necessary to put in place a workflow for rapidly processing the relatively large number of candidates being identified. The first step in this direction has been made by collating supporting information from in silico resources into a database, as described above. A web-based query interface has been built to provide access to these data (figure6.5) and this service is currently being used by colleagues via our laboratory intranet.

The next level will require what I refer to as medium-throughput screening—experimental validation in a series of standardised assays able to be run with rapid turnover. This will add to the existing network data by allowing experiments of a type unable to be run at a whole-genome scale. Steps are currently being taken to put such a system in place in our laboratory.

80 Integrated Gene Networks

The specific nature of the multi-gene interactions, however, will almost certainly preclude standard assays, thus a third level of validation will necessitate a more traditional experimental approach tailor-made to the system being studied.